You updated your password.

JavaScript seems to be disabled in your browser. For the best experience on our site, be sure to turn on Javascript in your browser.

Reset Password

Enter the email address you used to create your account. We will email you instructions on how to reset your password.

Forgot Your Email Address? Contact Us

Understanding Artificial Intelligence: Of Minds and Machines

Trailer

01: The Very Human History of AI

Friendly assistant or cold, rational killer? Science fiction reflects our ambivalence about artificial intelligence. Is it a good thing or bad? In this lecture, discover how AI already pervades daily life—from navigation apps to investing to weather forecasting. Trace its origins to Ada Lovelace, who worked with Charles Babbage in the early 1800s and foresaw the power of machine computation.

34 min

02: Intelligence: Real and Artificial

What exactly is AI? That depends on what we mean by intelligence. Compare rigid instinctual behavior with flexible, goal-directed problem solving in humans and animals. Consider theories of human intelligence—from multiple intelligences to the g-factor and the role of consciousness. Then, turn to AI to ask what genuine cognitive flexibility would require, and whether machines could ever achieve it.

31 min

03: The Foundations of Artificial Intelligence

From Aristotle’s logic to the 1956 Dartmouth Conference, the conceptual foundations of AI grew for centuries before the field finally took off. Explore the two rival traditions that emerged at Dartmouth: “Good Old-Fashioned AI,” seeking to replicate reasoning through logic and rules; and neural networks, inspired by the brain’s own architecture. Both approaches reached an impasse by the mid-1970s.

32 min

04: How AI Works

Descending in the 1970s, the long winter of artificial intelligence was eventually followed by the AI spring now blooming around us. Discover how neural networks—once considered dead ends—learned how to learn thanks to big data, limitless computational power, and new training methods. The outcome is today’s deep-learning systems, which have brought both astonishing promise and disquieting opacity.

31 min

05: Minds, Machines, and the Limits of AI

Can machines truly think or only simulate thought? Explore the limits of current AI: black-box opacity, where even designers can’t see how results arise, and overfitting, when systems learn their training data too well. Then, confront the philosophical puzzles of creativity, understanding, and mind through the “Chinese Room” thought experiment and the “hard problem” of consciousness.

30 min

06: AI Comes of Age

AI has amazed even its creators with two breakthroughs. Reinforcement learning has mastered games of strategy far beyond human skill, and ChatGPT-style systems have transformed the human-machine interaction via language. At heart, ChatGPT is a predictive program that associates a written input with a next word or phrase, drawing on a database of billions of web pages—and sounding eerily human.

32 min

07: AI and the Wisdom of Crowds

Explore the mixed blessing of relying on the internet’s aggregate wisdom for information, advice, and the seeming intelligence of AI. Surprising accuracy is possible when drawing conclusions from many independent sources, as in the classic “guess the ox’s weight” experiment. But when independence breaks down, the same approach can drive misinformation cascades and echo-chamber polarization.

30 min

08: Evolution and Artificial Intelligence

Follow Darwin’s idea of evolution by natural selection from biology into code. See how what works in nature also drives optimization in AI, where evolution-inspired algorithms automatically generate solutions to complex problems through variation and selection. Then, trace the evolution of the eye and its striking parallel in computer vision, where layered neural nets learn to see much like we do.

32 min

09: Ethics and AI

Look at Isaac Asimov’s Three Laws of Robotics from his classic science-fiction stories. Then, see how his fictional scenarios anticipated our own ethical dilemmas with AI, where algorithms make decisions that affect real lives. From biased parole software to self-driving cars forced into life-or-death trade-offs, today’s systems already test our moral frameworks. Should AI be regulated? If so, how?

31 min

10: Promises and Perils of AI

As powerful as AI is, we’ve dealt with transformative technologies before. Think of Gutenberg’s press, nuclear energy, or the genomic revolution—each bringing progress and peril. What do they teach us about managing AI? Explore its promise in medicine, science, and education, and its risks, including the phenomenon of AI increasingly feeding on its own output, thereby inevitably distorting our grasp on truth.

31 min

11: The Coming Singularity

Arnold Schwarzenegger’s cyborg assassin in The Terminator embodied the fear behind the “Singularity”—the moment when machines outthink and outmaneuver us. That idea now shapes serious debate over AI’s accelerating intelligence. Explore visions of a future where self-improving systems could render humanity irrelevant or even extinct, and the counterarguments that such fears may be greatly exaggerated.

31 min

12: Predicting Tomorrow’s AI

Look beyond the doomsday scenarios to consider what we might realistically expect from AI. On the positive side, advances in medicine, scientific discovery, and assistive technologies are already taking shape. On the negative side, we face rising waves of AI-generated junk content and escalating attacks on our privacy and the security of our digital infrastructure. Also certain: surprising new applications that we can’t yet foresee.

34 min

Overview Course No. 10660

Long before we were immersed in artificial intelligence, we met it in the movies—in HAL, R2D2, and the Terminator. Obsequious robots, homicidal mainframes, and virtual companions prepared us for a future that has now arrived, with chatbots, autonomous systems, financial algorithms, and other applications. With AI seemingly everywhere, it is crucial to understand how it works.

In Understanding Artificial Intelligence: Of Minds and Machines, philosopher Patrick Grim traces the story of AI from ancient legends to the neural networks behind today’s breakthroughs. You see how modern systems became so powerful so quickly. You also explore what “intelligence” means and how AI achieves its flexibility: massive datasets that let systems learn from examples, deep-learning architectures inspired by the brain, and feedback loops that help models improve. Together, these give AI its uncanny mimicry of human thought.

The course also confronts AI’s risks—black-box opacity, deepfakes that erode shared reality, and the much-debated “Singularity,” the point at which machines might one day outstrip human intelligence.

Understanding how AI works is the best strategy for navigating what comes next. AI may slow as it matures—or future advances like quantum computing may push it far beyond today’s limits. As Jean-Jacques Rousseau warned, it is crucial “to foresee that some things cannot be foreseen.”

About

Patrick Grim

The astounding technologies that surround us, as well as the ethical challenges those technologies bring, are the result of a very human history.

ALMA MATER

Boston University

INSTITUTION

Stony Brook University and University of Michigan

Patrick Grim is Distinguished Teaching Professor of Philosophy Emeritus at Stony Brook University and Philosopher in Residence with the Center for the Study of Complex Systems at the University of Michigan. He earned his BPhil from the University of St. Andrews, Scotland, and his PhD from Boston University. He has published extensively in scholarly journals across a range of disciplines and has received numerous awards for his teaching. His books include The Incomplete Universe, The Philosophical Computer, and Theory of Categories.